A neural network model has been made of the transfer of visual information between LGN and layer 4C of macaque primary visual cortex. It tests the hypothesis that purely feedforward, excitatory convergence of P- and M-inputs onto layer 4C spiny stellate neurons is sufficient to explain the experimentally observed gradual change of receptive field size and contrast sensitivity with depth in the layer. The connectionist neural network model is based on the different receptive field sizes and achromatic contrast sensitivities of LGN M and P cells, size and overlap of individual M or P axon arbors in 4C, segregation of M and P terminals in layer 4C and the size of the postsynaptic cells; it shows that the transformation from discrete input channels to a gradient of physiological properties can be achieved in lower 4C$\\beta$ and mid-4C by dendritic overlap of the postsynaptic cells between M and P input zones alone. By splitting the M population in two distinct groups, where those cells with the most extreme physiological parameters project exclusively to upper 4C$\\alpha$, a good fit was achieved for the gradient of physiological properties through depth of 4C. The properties of the two M inputs were constrained by physiological data for LGN M cells and based on anatomical evidence for the existence of a second group of M fibers entering upper 4C$\\alpha$, which is predicted by our model to make up at most $12 \\\\%$ of the M-population. The transformation from the two distinct M and P properties to a gradient of change is of considerable importance to understanding properties of the three output pathways from layer 4C to more superfical laminae of primary visual cortex, which in turn relay information to different extrastriate areas. \\\\ Supported by NEI-EY10021, MRC9203679N, MRC9408137, HFSPO grant RG-98/94, DFG scholarship 231 and DFG grant Ob-102/2-1.